Although our method is designed for low-shot settings, it is interesting to evaluate its performance in the standard WSOD setting as well.
Related to our work is transfer learning approaches for large-scale WSOD [1]}, [2]} with ImageNet detection as the standard benchmark.
Typically, the first 100 classes are used as the base dataset and the rest 100 with 65k images are used as novel objects.
We follow the setup in [1]} and use the pre-trained Inception-Resnet Faster-RCNN model and weights provided by the authors to extract proposals, features, and the objectness scores.
We apply our EM algorithm to the extracted features and use \(u^-_{\mathbf {\omega }}(\mathbf {x})=\alpha (1-\rm {obj}(\mathbf {x}))\)  for each proposal \(\mathbf {x}\)  where \(\rm {obj}(\mathbf {x})\)  is the Faster-RCNN objectness score and \(\alpha \)  is a hyper-parameter we tuned for the task.
To our surprise, vFM-MIL outperforms [1]} in Table REF  while being about \(100\times \)  faster. We believe this results can be further improved by relaxing some of the assumptions in our statistical model as overfitting may not be as significant in large-scale. For instance, we can learn a separate concentration parameter for each novel class in the EM steps. Furthermore, we can utilize the novel dataset to update the background scoring function \(u^-_\omega \) . We defer these improvements and further analysis to the future work.
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